Joint User Association and Resource Allocation for Wireless Hierarchical Federated Learning with Non-IID Data

Shengli Liu, Guanding Yu, Xianfu Chen, Mehdi Bennis

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

Wireless hierarchical federated learning (HFL) has been proposed for large-scale model training over multi-cell network while preserving the data privacy. However, the imbalanced data distribution and load have a significant impact on the convergence rate, the learning accuracy, and the learning latency in wireless HFL with non-independent identically distributed training data. To cope with these challenges, we first derive the learning latency and the upper bound of the model error. Then, an optimization problem is formulated to minimize the weighted sum of total data distribution distance and learning latency. Joint user association and wireless resource allocation algorithms are investigated to achieve the optimal learning performance. Finally, the effectiveness of the proposed algorithms are demonstrated by the simulations.

Original languageEnglish
Title of host publicationICC 2022 - IEEE International Conference on Communications
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages74-79
Number of pages6
ISBN (Electronic)978-1-5386-8347-7
DOIs
Publication statusPublished - 2022
MoE publication typeA4 Article in a conference publication
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: 16 May 202220 May 2022

Conference

Conference2022 IEEE International Conference on Communications, ICC 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period16/05/2220/05/22

Fingerprint

Dive into the research topics of 'Joint User Association and Resource Allocation for Wireless Hierarchical Federated Learning with Non-IID Data'. Together they form a unique fingerprint.

Cite this